A Low-power SoC-based Moving Target
Detection System for Amphibious Spherical Robots
Shaowu Pan
1, 2, 3
, Liwei Shi
1, 2, 3,*
, Shuxiang Guo
1, 2, 3, 4
, Ping Guo
1, 2, 3
, Yanlin He
1, 2, 3
and Rui Xiao
1, 2, 3
1
The Institute of Advanced Biomedical Engineering System, School of Life Science, Beijing Institute of Technology, No.5,
Zhongguancun South Street, Haidian District, Beijing 100081, China
2
Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, the Ministry of Industry and Infor-
mation Technology, Beijing Institute of Technology, No.5, Zhongguancun South Street, Haidian District, Beijing 100081, China
3
Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing Institute of Technology, No.5, Zhongguan-
cun South Street, Haidian District, Beijing 100081, China
4
Faculty of Engineering, Kagawa University, 2217-20 Hayashi-cho, Takamatsu, Kagawa, Japan
panshaowu.hit@gmail.com, shiliwei@bit.edu.cn, guoshuxiang@bit.edu.cn
*
Corresponding author
Abstract – A moving target detection and tracking system is
critical important for autonomous mobile robot to accomplish
complicated tasks. Aiming at application requirements of our
amphibious spherical robot proposed in previous researches, a
low-power and portable moving target detection system was de-
signed and implemented in this paper. Xilinx Zynq-7000 SoC
(System on Chip) was used to fabricate the image processing
system of the robot for detection and tracking. An OmniVision
OV7670 COMS image sensor controlled by customized IP cores
in the PL (Programmable Logic) of the SoC was adopted to ac-
quire 640×480 RGB images at 30 frames per second. The Gaus-
sian background modeling method was implemented with Vivado
HLS in the PL to detect moving targets. And a FCT (Fast Com-
pressive Tracking) tracker with motion estimation mechanism
was running in the PS (Processing System) of the SoC to track
targets captured by the detection subsystem subsequently. Be-
sides, the dynamical power management (DPM) and the dynami-
cal voltage frequency scaling (DVFS) mechanisms were used for
a higher power-efficiency. Experimental results verified the vali-
dation and performance of the detection system. The design in
this paper may have reference value for vision-based mobile ro-
bots or vehicles.
Index Terms – Moving target detection. Gaussian background
modeling. Low-power. Zynq-7000 SoC. Amphibious spherical ro-
bot.
I. INTRODUCTION
As one of the most effective and feasible tools to sense the
surroundings [1], digital cameras have been widely used in
mobile robots to realized intelligent methods. Among various
vision systems of a mobile robot, the moving target detection
and tracking system often plays a critical important role to
realize robotic functions, such as autonomous navigation [2],
visual servoing [3], path planning [4], robot–human interac-
tion [5] and et al.
Target detection is the process to probe motions or beha-
viors of targets by analyzing the scenarios in image sequences
and then mark coordinates of the moving targets [6]. The most
common detection algorithms include the background subtrac-
tion method [7], the optical flow method [8], the template
matching method [9], etc. The background subtraction method
extracts the foreground or target by calculating the error be-
tween an image from the video and the image of background
[10]. The optical flow method detects moving targets by cal-
culating the optical flow field of images and then analyzing
the velocity vector features [11]. The template matching me-
thod, which is usually used for stationary target detection or
works as an assist module for robust target tracking, locates
specific targets by compare features such as color [12], con-
tour [13], SIFT (Scale Invariant Feature Transform) [14], etc.
In recent years, most researchers have combined studies of
target detection with machine learning theories, and tried to
improve the precision of detection with pattern classification
algorithms [15]. And some state-of-the-art detection algo-
rithms, which were built upon SVM (Support Vector Ma-
chine) [17], Adaboost [18], wavelet [19], etc., have been pro-
posed.
However, most related studies mainly aimed at improving
the precision of detection, while the computational consump-
tion and usability of algorithms were sometimes overlooked.
Unlike theoretical studies conducted on high performance
desktops or workstations, target detection and tracking for
robotic applications are usually based on embedded micropro-
cessors and limited power supply. Thus the real-time perfor-
mance of the detection algorithm and the power consumption
of the whole system have to be taken into consideration.
FPGA (Field Programmable Gate Array) [19] and DSP (Digi-
tal Signal Processor) [20] were adopted to implement real-
time portable detection and tracking systems in some studies,
but few studies pay close attention to low-power system de-
sign and global power optimization [21].
To meet requirements of our amphibious spherical robots,
which necessitated even stricter constraints on power con-
sumption and heat of circuits, a novel low-power moving tar-
get detection system was designed and implemented in this
paper. The latest Xilinx Zynq-7000 SoC (System on Chip)
was adopted to fabricate the electrical system of our robot for
control and vision applications. OmniVision OV7670, which
is a compact CMOS camera controlled by IP cores in the PL
(Programmable Logic) of the SoC, was used to capture
640×480 RGB images at the rate of 30 fps (frames per
second). The Gaussian background modeling method was im-
plemented with Vivado HLS in the PL of the SoC to detect
1116
978-1-4799-7096-4/15/$31.00 ©2015 IEEE
Proceedings of 2015 IEEE
International Conference on Mechatronics and Automation
August 2 - 5, Beijing, China
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